The performance of the proposed framework and of the state-of-the-art methods on the GDXray [1]} dataset are reported in Table REF . We can observe here that the proposed framework outperforms the CST [2]} and the TST framework [3]} by 4.98% and 1.07%, respectively.
Furthermore, we wanted to highlight the fact that CST [2]} is only an object detection scheme, i.e., it can only localize the detected items but cannot generate their masks. Masks are very important for the human observers in cross-verifying the baggage screening results (and identifying the false positives), especially from the cluttered and challenging grayscale scans.
In Figure REF , we report some of the cluttered and challenging cases showcasing the effectiveness of the proposed framework
in extracting the overlapping contraband items. For example, see the extraction of merged knife instances in (H), and the cluttered shuriken in (J, L). We can also appreciate how accurately the razors have been extracted in (J, L). Extracting such low contrast objects in the competitive CST framework requires suppressing first all the sharp transitions in an iterative fashion [2]}.
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